Expectation-Maximization χ Self-Organizing Maps for Image classification

Thales Sehn Korting; Leila Maria Garcia Fonseca; Fernando Bação

doi:10.1109/SITIS.2008.35

Expectation-Maximization χ Self-Organizing Maps for Image classification

Thales Sehn Korting, Leila Maria Garcia Fonseca, Fernando Bação

NOVA Information Management School (NOVA IMS)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

To deal with the huge volume of information provided by remote sensing satellites, which produce images used for agriculture monitoring, urban planning, deforestation detection and so on, several algorithms for image classification have been proposed in the literature. This article compares two approaches, called Expectation-Maximization (EM) and Self-Organizing Maps (SOM) applied to unsupervised image classification, i.e. data clustering without direct intervention of specialist guidance. Remote sensing images are used to test both algorithms, and results are shown concerning visual quality, matching rate and processing time.

Original language	English
Title of host publication	SITIS 2008 - Proceedings of the 4th International Conference on Signal Image Technology and Internet Based Systems
Pages	359-365
Number of pages	7
DOIs	https://doi.org/10.1109/SITIS.2008.35
Publication status	Published - 2008
Event	4th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2008 - Bali, Indonesia Duration: 30 Nov 2008 → 3 Dec 2008

Conference

Conference	4th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2008
Country	Indonesia
City	Bali
Period	30/11/08 → 3/12/08

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10.1109/SITIS.2008.35

Cite this

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title = "Expectation-Maximization χ Self-Organizing Maps for Image classification",

abstract = "To deal with the huge volume of information provided by remote sensing satellites, which produce images used for agriculture monitoring, urban planning, deforestation detection and so on, several algorithms for image classification have been proposed in the literature. This article compares two approaches, called Expectation-Maximization (EM) and Self-Organizing Maps (SOM) applied to unsupervised image classification, i.e. data clustering without direct intervention of specialist guidance. Remote sensing images are used to test both algorithms, and results are shown concerning visual quality, matching rate and processing time.",

author = "Korting, {Thales Sehn} and Fonseca, {Leila Maria Garcia} and Fernando Ba{\c c}{\~a}o",

year = "2008",

doi = "10.1109/SITIS.2008.35",

language = "English",

isbn = "9780769534930",

pages = "359--365",

booktitle = "SITIS 2008 - Proceedings of the 4th International Conference on Signal Image Technology and Internet Based Systems",

note = "4th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2008 ; Conference date: 30-11-2008 Through 03-12-2008",

}

Korting, TS, Fonseca, LMG & Bação, F 2008, Expectation-Maximization χ Self-Organizing Maps for Image classification. in SITIS 2008 - Proceedings of the 4th International Conference on Signal Image Technology and Internet Based Systems., 4725827, pp. 359-365, 4th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2008, Bali, Indonesia, 30/11/08. https://doi.org/10.1109/SITIS.2008.35

Expectation-Maximization χ Self-Organizing Maps for Image classification. / Korting, Thales Sehn; Fonseca, Leila Maria Garcia; Bação, Fernando.

SITIS 2008 - Proceedings of the 4th International Conference on Signal Image Technology and Internet Based Systems. 2008. p. 359-365 4725827.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Fonseca, Leila Maria Garcia

AU - Bação, Fernando

PY - 2008

Y1 - 2008

N2 - To deal with the huge volume of information provided by remote sensing satellites, which produce images used for agriculture monitoring, urban planning, deforestation detection and so on, several algorithms for image classification have been proposed in the literature. This article compares two approaches, called Expectation-Maximization (EM) and Self-Organizing Maps (SOM) applied to unsupervised image classification, i.e. data clustering without direct intervention of specialist guidance. Remote sensing images are used to test both algorithms, and results are shown concerning visual quality, matching rate and processing time.

AB - To deal with the huge volume of information provided by remote sensing satellites, which produce images used for agriculture monitoring, urban planning, deforestation detection and so on, several algorithms for image classification have been proposed in the literature. This article compares two approaches, called Expectation-Maximization (EM) and Self-Organizing Maps (SOM) applied to unsupervised image classification, i.e. data clustering without direct intervention of specialist guidance. Remote sensing images are used to test both algorithms, and results are shown concerning visual quality, matching rate and processing time.

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ER -

Expectation-Maximization χ Self-Organizing Maps for Image classification

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